Pricing policies too low and selling too many is a risky strategy for insurance carriers. Simply stoking volume growth in policies while ignoring the bottom line has often led to a forced market exit of property-casualty insurers as well as other risk-management enterprises.
Alternatively, securing the right premium for the right risk is the name of the game–as is the continual segmentation and management of portfolios, which consider competitor reactions and customer choices, as well as the productivity of agents, clients and markets. Collectively, such informed business analysis can present new possibilities for the modern insurance enterprise in today's restrictive economy.
There are many challenges as well. Premium leakage, insurance-to-value, rate adequacy, underwriting expenses, commissions by channel, loss control and the basis of claims costs all point to the common predicament of effectively analyzing risk. The problem can be even more complex as risk profiles change over time, sometimes quickly and within finite policy periods.
Nevertheless, the availability of more and better data, higher computing power and predictive modeling present insurers with fresh reserves of insight to quantify risk, retain their best customers and achieve organic growth.
A range of underwriting efficiencies is evolving from the application of sophisticated business intelligence and predictive modeling. To fill in key gaps for risk and causation, insurers are tapping into nontraditional structured data and making valuable business correlations from unstructured text sources.
Calculations of risk and underwriting are being enhanced by innovations in areas such as geospatial information and algorithms that examine the interaction of stakeholders and information within and across the insurance value chain.
To calibrate data integrity and underwriting programs in commercial, specialty and personal lines, enterprise data-management methods are now being engaged to fuel dynamic predictive models, where risk data transforms over time.
In that light, extensive use of analytic-based data cross-validation and authentication strategies can bring more stability to the underwriting process.
Data accuracy and resultant analytics are proving to be critical in key underwriting functions. They help analyze the changing nature of real-world perils, catapulting early-adopter companies into market leadership, in large part because of increased precision in managing expenses while quickly executing on risk-based pricing implementations.
There are plenty of marketplace rewards for companies that leverage the power of interrelated data from inside their organizations (claims, actuarial, finance, marketing, pricing, distribution, operations, risk management, catastrophe management and customer service) and from outside (comparative raters, competitor filings, consumer choice, regulatory guidance, service channels, third-party vendors and even advertising spending).
In addition to traditional analysis, the art and science of advanced data analytics now incorporate features and dimensions derived from third-party sources.
This convergence of rich data and modeling expertise expands the diversity and utilization of contextual information in the underwriting process. Only modern analytic science can make practical sense of this wealth of data, including geospatial statistics, text, operational interactions and time-sequenced patterns from linked data across multiple systems.
Risk-based modeling methods improve rate-making engines with stronger data, often leading to competitive advantage.
For example, in the auto line of business, some companies have improved their market share even as others have diminished.
In contrast, the cumulative value and the aggregation of exposure in a concentrated property loss event can debilitate carriers with limited or unfocused underwriting intelligence. Property underwriters are thus deploying analytics to expand their understanding of exposures and risk considerations, especially by incorporating catastrophe probabilities.
Using analytics in conjunction with other decision-support tools, carriers can refine risk selection and how they interleave exposures.
Maintaining underwriting discipline based on good predictive analytics may sometimes mean that insurers must forgo a volatile market segment. At other times, embracing the segment may make more sense. But no matter what choice insurers make, acting on such intelligence can be an important survival tactic.
Looking ahead, companies are further investing in analytics as the basis for risk decision-making and smart underwriting. A commitment to predictive modeling allows multiline carriers to gain valuable, perhaps hidden, perspectives.
Everything those carriers learn–in personal and commercial lines underwriting, marketing, and reported claims–can be extended across their organizations and distribution channels. This knowledge can help make agents more productive and allows companies to manage essential underwriting personnel while improving overall expense efficiency.
Better insights about the perils where customers live and drive are being discovered and used with thoughtful analysis of personal lines risks for auto and home. Some analytic solutions may take years to reach market.
However, traditional geographic rating, vehicle classification criteria and driver plan assignment are continuously being enhanced through analytics-based risk assessment.
Commercial vehicles–both trucking and fleet–have long used vehicle location technologies for asset management, logistics planning, driver management and accident/incident event recording.
Now, commercial lines carriers are taking advantage of the convergence of predictive modeling and technology initially honed in personal lines.
Emerging technologies such as vehicle-generated data can track and analyze driver behavior, vehicle performance, driving distances, hazard monitoring, driver/vehicle interactions and mechanical maintenance.
Onboard instrumentation can also alert drivers to dangerous conditions and optimize route planning for on-time delivery, accident avoidance, fuel consumption and carbon-footprint minimization.
In the personal lines arena, vehicle-generated data–from devices that policyholders would opt to use in their vehicles–may eventually provide rating variables for pay-as-you-drive premium options. Already, the possibility of dynamically generated route-based insurance is being considered by commercial fleet providers for both in-transit and at-rest periods of exposure and insurability.
Insurers are rapidly establishing analytical insights throughout their enterprises, enabling measurable efficiencies for operations, agent support and customer service. Well-targeted retention programs and intelligently placed marketing strategies are providing higher returns.
Organizations can act more consistently and reengineer critical processes in light of expanded, real-time enterprise knowledge.
But to make such multifaceted intelligence even more actionable, insurers need a coordinated infusion of technology, talent, dedication and vision. Those that execute fastest to integrate quality data, analytics and decision support will achieve greater success, now and over time.
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